Multiband Envelope Spectra Extraction for Fault Diagnosis of Rolling Element Bearings

Abstract

Bearing fault features are presented as repetitive transient impulses in vibration signals. Narrowband demodulation methods have been widely used to extract the repetitive transients in bearing fault diagnosis, for which the key factor is to accurately locate the optimal band. A multitude of criteria have been constructed to determine the optimal frequency band for demodulation. However, these criteria can only describe the strength of transient impulses, and cannot differentiate fault-related impulses and interference impulses that are cyclically generated in the signals. Additionally, these criteria are easily affected by the independent transitions and background noise in industrial locales. Therefore, the large values of the criteria may not appear in the optimal frequency band. To overcome these problems, a new method, referred to as multiband envelope spectra extraction (MESE), is proposed in this paper, which can extract all repetitive transient features in the signals. The novelty of MESE lies in the following aspects: (1) it can fuse envelope spectra at multiple narrow bands. The potential bands are selected based on Jarque-Bera statistics of narrowband envelope spectra; and (2) fast independent component analysis (fastICA) is introduced to extract the independent source spectra, which are fault- or interference-related. The multi-band strategy will preserve all impulse features and make the method more robust. Meanwhile, as a blind source separation technique, the fastICA can suppress some in-band noise and make the diagnosis more accurate. Several simulated and experimental signals are used to validate the efficiency of the proposed method. The results show that MESE is effective for enhanced fault diagnosis of rolling element bearings. Bearing faults can be detected even in a harsh environment.

Optimal SES Selection Based on SVD and Its Application to Incipient Bearing Fault Diagnosis

“…When rolling elements run over the defect area, such as the crack occurring on bearing component, a series of impulses will be generated and thus excite the resonance of the bearing or adjacent components at a certain rate which is called "bearing characteristic frequencies." e vital issue for bearing fault diagnosis is to detect these characteristic frequencies [31].…”

Slow Degradation Fault Detection in a Harsh Environment

“…Fault detection procedures can be divided into three main categories: signal processing techniques [3]- [6], modelbased techniques [7]- [9], and data-driven ones [10]- [16]. In this work we will focus in the latter, where data will drive the identification of normal and faulty modes of operation.…”

Fault Diagnosis: Spectral Analysis of the Vibration Signals in Transfer Press

“…There exist several studies focusing on fault diagnosis mostly using vibration data acquired from rotating machinery or rolling-element bearings. For example, it was found by various researchers that envelope analysis is a powerful tool for diagnostics of rolling-element bearing signals [1,2,3]. In [1], authors compared cyclostationary and envelope analysis on vibration data for diagnosing of rolling-element bearings and they reported that envelope analysis is advantageous over cyclostationary analysis (spectral correlation).…”

“…Thus, squared envelope analysis was found to be an important method to overcome this problem [2]. A multiband envelope spectral extraction technique was proposed to tackle with determining optimal frequency band for narrow band demodulation using vibration signals for fault diagnosis in [3]. In previous studies it was observed that envelope spectrum is randomly distributed over the frequency.…”